The Role of Selected Factors in the Development and Consequences of Alcohol
Dependence

Rebecca Gilbertson; Robert Prather; and Sara Jo Nixon,
Ph.D.

REBECCA GILBERTSON is a doctoral candidate; ROBERT PRATHER
is senior project coordinator; and SARA JO NIXON, PH.D., is professor and chief,
all in the Division of Clinical Addiction Research, Department of Psychiatry,
University of Florida, Gainesville, Florida.

Gender, family
history, comorbid psychiatric and substance use disorders, and age all influence
a person’s risk for alcoholism. In addition, these factors interact with
alcoholism to influence neurocognitive functioning following detoxification. This
article examines these factors and considers how they interact with each other.
This complexity reinforces the need for both animal and human studies and suggests
multiple factors that may be sensitive to differential prevention, intervention,
and treatment efforts. Thus, it is imperative that hypothesis-driven research
designs be directed to identifying the relative potency of these factors and their
interactions. Key words: Alcoholism; alcohol and other drug (AOD) dependence;
risk factors; genetic factors; family factors; environmental factors; gender differences;
family AOD use (AODU) history; comorbidity; multiple drug use; age of AODU onset;
AOD effects; brain

Many risk factors contribute to both the development
of alcohol dependence and its long-term consequences. This complexity no doubt
contributes to the heterogeneity in research findings, complicating treatment
as well as identifying multiple avenues for intervention efforts. A comprehensive
review of the risk factors for alcoholism is beyond the scope of this article.
Rather, the following sections will focus on five major risk factors: gender,
family history, psychiatric comorbidity, comorbid substance abuse, and age. In
addition to discussing how these factors influence alcoholism risk, the article
also will examine how they interact with alcoholism to influence neurocognitive
functioning following detoxification. Thus, information in this article focusing
on neurocognitive performance in alcoholics generally refers to data obtained
from people who are recently sober, beyond the stage of detoxification, and not
currently on medication that might affect neurocognitive function. Although this
condition restrains the generalizability of the results, it provides a more considered
review of the neurocognitive impact of alcohol dependence.

Before proceeding,
it is necessary to clarify and define terms used in this article. Throughout the
past two decades, the clinical definition of “alcoholic” and “alcoholism”
has evolved, as evidenced in classification issues detailed in the Diagnostic
and Statistical Manual of Mental Disorders (DSM) used by mental health professionals
(American Psychiatric Association [APA] 1980, 1987, 1994). For example, as programmatic
research was being broadly initiated in the 1970s and 1980s, research inclusion
criteria often did not differentiate between alcohol abuse and dependence. Thus,
studies often included participants with either disorder in a single group referred
to as those with “an alcohol use disorder” or “alcoholism.”
As the complexity of alcohol use disorders (AUDs) was better appreciated and clinically
and scientifically meaningful distinctions between alcohol-related diagnoses were
made, groups within studies became more strictly defined. Although it is not universally
accepted, the term “alcoholic” now is generally applied within addiction
research to people with a DSM–IV “alcohol dependence” diagnosis.
Given this shift in perspective, it is important to recognize that earlier studies
(e.g., those including data from the 1970s, 1980s, and early 1990s) may include
people with either or both diagnoses.

Gender

Gender As a Risk
Factor for Alcohol Dependence

Researchers have investigated drinking behaviors,
their etiology, and outcomes among women for several decades (e.g., Fabian et
al. 1984; Glenn and Parsons 1992; Nixon and Glenn 1995; Sullivan et al. 2002).
These data suggest that the rate of progression of problematic drinking and subsequent
risk for alcohol- related consequences may be different for men and women. Historically,
men have reported an earlier age of onset of alcohol use initiation than women
(Chou and Dawson 1994; Gomberg 1993). One large national study (Project MATCH)
(Randall et al. 1999) of people seeking treatment for alcoholism supported these
findings and further showed that men displayed evidence of problematic drinking
behaviors (i.e., regular intoxication, loss of control over drinking) earlier
than women.

However, not all studies support gender
differences in age of onset of regular alcohol use, and some suggest that age
of initial use may be increasingly similar for both genders, at least for those
who ultimately seek treatment. For example, Hernandez-Avila and colleagues (2004)
found remarkable similarity in age of onset of regular use between male and female
substance abusers, reporting no significant differences between men and women
with current alcohol dependence diagnoses with regard to age of onset of regular
drinking or age of onset of regular alcohol intoxication. They did, however, find
that women progressed from regular use to treatment more quickly than men (see
figure 1). This latter finding is consistent with other data demonstrating that
women progress through the stages of regular intoxication, drinking problems,
and loss of control over drinking more quickly than men. That is, women demonstrate
a “telescoping” of disease progression and experience more severe
consequences more quickly (Diehl et al. 2007; Hernandez-Avila et al. 2004; Mann
et al. 2005; Randall et al. 1999).

The telescoping effect in alcoholic
women may be associated with several factors. First, the immediate personal and
professional costs to women may be greater, as suggested by data indicating that
women report more psychiatric, medical, and employment consequences from heavy
drinking compared with men (Hernandez-Avila et al. 2004). Second, telescoping
may be related to gender differences in physiology. For example, among men and
women consuming similar amounts of alcohol (per body weight) (Mann et al. 2005),
women may experience higher blood alcohol concentrations because of metabolic
differences (see Ammon et al. 1996; Baraona et al. 2001; Frezza et al. 1990).
Thus, certain complications that may contribute to the telescoping effect in alcoholic
women could be attributed to achieving and sustaining higher blood alcohol levels
than alcoholic men when equivalent doses of alcohol are consumed.

Neuroimaging techniques have allowed
further investigation of the macrostructural (i.e., the size or volume of a brain
structure) and microstructural (i.e., the small constituents of white matter,
such as myelin) integrity of white matter tracts within the brains of alcoholic
men and women (Pfefferbaum et al. 2002, 2006; Pfefferbaum and Sullivan 2002).
Although alcoholic men were observed to have macrostructural aberrations (including
smaller volume) in the pons, corpus callosum, and cortical white matter, alcoholic
women did not display such differences (Pfefferbaum et al. 2002). However,
the microstructural integrity of cortical and callosal white matter was affected
to similar extents in both alcoholic men and women, even though the alcoholic
women had drunk far less alcohol in their lifetimes than the men (Pfefferbaum
and Sullivan 2002). These results suggest that white matter areas within
the brain are affected by alcohol dependence; however, they may be affected differently
in women compared with men. Further, although certain areas may not show
overt volume differences, alcohol dependence still may affect the microstructural
integrity and potentially compromise brain function. Research is ongoing regarding
the microstructural integrity of the brain following alcohol dependence, with
results suggesting the involvement of multiple brain regions (Pfefferbaum et al.
2006).

Despite the strength of these findings, it should again be
noted that much of the work conducted with women has not fully accounted for metabolic
(pharmacokinetic) differences between the genders. These differences result in
greater alcohol exposure of liver and brain tissue in women as opposed to men,
even when an equivalent dose of alcohol is consumed (Baraona et al. 2001; Dettling
et al. 2007; Frezza et al. 1990; Hommer et al. 2001). Thus, women may not be differentially
sensitive to alcohol, per se, but rather may be chronically exposed to higher
blood alcohol levels even at lower doses.

Family History

Family
History As a Risk Factor for Alcohol Dependence

It is well established
that alcoholism runs in families. Furthermore, adoption studies, family pedigree
studies, and twin studies consistently support the role of genetic risk rather
than familial transmission for alcohol dependence (Carlson et al. 2002; Cloninger
et al. 1981; Cotton 1979; McGue 1997; Russell 1990). Estimates vary, but it generally
is accepted that offspring of alcoholics are approximately four times more likely
to develop alcoholism than people without such a history (Russell 1990), even
if they are not reared with an alcoholic parent. Most early research studied male
offspring of male alcoholics. This limitation led to the early conclusion that
men were more likely to experience the familial form of the disease, whereas women
were more likely to experience a reactive form associated with psychiatric comorbidity,
empty-nest syndrome, or related factors.

With continued and more broadly
developed research, these assumptions have been modified. Widely cited studies
using male and female monozygotic and dizygotic twins suggest that genes, environment,
and their interaction are potent contributors to the development of alcohol dependence
in both genders (Heath et al. 1997; Krueger et al. 2002; McGue 1997, 1999; Prescott
and Kendler 1999; Sigvardsson et al. 1996) (see figure 2). Approximately 40 percent
of the variance for alcoholism onset in men (Prescott and Kendler 1999) and 60
percent of this variance in women can be attributed to genes (Kendler et al. 1992).

Many twin studies considered paternal alcoholism rather than both paternal and
maternal input (Kendler et al. 1992). Attention to direct maternal contribution
has been limited for numerous reasons. One of the predominant reasons is that
the study of maternal genetic impact on alcoholism risk was restrained by the
concern that offspring would be more likely to be exposed to alcohol in utero,
and, thus, results regarding genetic risk would be confounded with the effects
of early exposure (see Streissguth and O’Malley 2000). However, Hill and
colleagues (e.g., Hill and Steinhauer 1993; Hill et al. 1995), controlling for
prenatal exposures, demonstrated that daughters of alcoholic mothers also were
at increased risk for alcoholism, even without paternal alcoholism.

The
Collaborative Study on the Genetics of Alcoholism (COGA), in conjunction with
other studies, has implicated several genetic markers in which variations appear
to increase risk for alcohol dependence and related disorders. These include genes
associated with the acetylcholine receptor, the receptor for the major inhibitory
neurotransmitter, γ-aminobutyric acid (GABA), and those associated with alcohol
metabolism (see Edenberg and Foroud 2006; Porjesz and Rangaswamy 2007). Edenberg
and Faroud (2006) also reported preliminary data on several other loci, one of
which is associated with the bitter taste receptor. Agrawal and colleagues (2008)
extended work with the COGA sample and further expanded the list of potential
genes by implicating regions of chromosomes believed to affect neurophysiology
in complex ways, including signal transduction across cell membranes within the
brain. This group also has implicated the role of signal transduction in modulating
risk in an additional study (Dick et al. 2007). If, as noted above, an estimated
40 to 60 percent of the risk for alcoholism can be attributed to genetic factors,
a sizable remaining variance is associated with environmental factors and gene-by-environment
interactions. Finnish and Canadian twin studies (Jang et al. 2000, 2001; Kaprio
et al. 2002) indicate that environmental factors such as geographical locations
with high consumption rates, religiosity/moral views, and exposure to antisocial
personality traits may interact with genetics to increase risk for alcohol dependence.
Although some studies suggest that exposure to paternal alcoholism during childhood
does not seem to contribute to greater risk for alcoholism later in life (Duncan
et al. 2006), other studies show that a low-risk environment (i.e., absence of
paternal alcoholism) can reduce the risk of developing alcoholism later in life
even in people with greater family density of alcoholism (Jacob et al. 2003).
Thus, both environmental and genetic factors influence risk for alcohol dependence
and related disorders.

Family History As a Factor in Alcohol’s Effects
on the Brain

Not only does a positive family history increase the risk
for developing alcoholism, it also may influence neurocognitive functioning among
people who have such a history but are not themselves alcoholic. For example,
several studies have examined mental processes in offspring (primarily sons) of
male alcoholics (Giancola et al. 1996; Tarter et al. 2003). These studies have
observed subtle, yet significant, deficits among family history positive (FH+)
participants, particularly on tasks such as problem solving and abstraction, often
referred to as executive cognitive functioning (ECF) (Aytaclar et al. 1999; Giancola
et al. 1996; Tarter 2002).

Other studies
have examined neurophysiological functioning in FH+ nonalcoholics.
Many of these studies have used noninvasive brain electrophysiology to measure
the brain’s electrical responses with electrodes placed on the scalp. These
studies suggest aberrations in the neurophysiology underlying target detection,
memory updating, and working memory in both male and female offspring of alcoholics
(Begleiter et al. 1984; Carlson et al. 2004; Hill et al. 1995; Rangaswamy et al.
2007). Importantly, however, such aberrations are not uniformly observed, and
researchers have documented eventual normalization of these responses in subgroups.
Thus, it appears that although some FH+ individuals may demonstrate
long-lived, yet subtle, deficits in these measures; for others, these deficits
suggest a development lag in fundamental brain processes (Bauer and Hesselbrock
1999; Hill et al. 1999; Hill and Shen 2002).

Additional studies have used
neuro­imaging procedures such as magnetic resonance imaging (MRI) or related
procedures to examine brain function in FH+ individuals (Hill et al.
2007; McNamee et al. 2008). These studies also have reported brain changes in
FH+ adolescents, showing decreased activation in the frontal region
of the brain (an area typically associated with ECF) as well as areas of the brain
associated with social cognition and empathy. Additionally, brain response to
alcohol cues may differ between FH+ and FH- individuals.
Bartholow and colleagues (2007) found that FH+ individuals had greater
P3001 [1P300 refers to a positive event-related potential
wave recorded via electroencephalography at about 300 to 600 milliseconds. This
signal often is used as a measure of cognitive function.] amplitude response to
alcohol cues versus nonalcohol cues.

It remains unclear the extent to
which these aberrations or alterations in brain function serve as markers for
risk for developing alcohol dependence or whether they reflect more general behavior
patterns associated with disorders that commonly co-occur with alcohol dependence,
such as childhood behavior disorders or other externalizing disorders.

Because (1) the majority of chronic alcohol studies are conducted using treatment-seeking
alcoholics and (2) the large majority of treatment-seeking alcoholics have positive
family histories, there has been some question to whether neurocognitive deficits
(including behavioral and neuroimaging aberrations) are associated with FH+
status rather than alcohol dependence, per se. Because so few studied alcoholics
are FH-, it is difficult to make statistically sound comparisons. Parsons
and colleagues (see for example Glenn and Parsons 1989; Parsons 1994) conducted
a series of studies explicitly examining this hypothesis. The studies compared
FH+ and FH- detoxified alcoholics without confounding psychiatric
(e.g., schizophrenia or bipolar disorders) or medical (e.g., epilepsy or history
of head injury) disorders on a battery of neuropsychological tests. Consistent
with the hypothesis that chronic excessive alcohol use is neurotoxic, alcoholic
participants demonstrated deficits that could not be accounted for by FH (see
figure 3). Interestingly, a recent study (Fein and Chang 2008) considering the
role of FH found that density of FH, rather than alcohol use, was negatively associated
with impaired decisionmaking ability in alcoholics. Specifically, alcoholics with
a greater density of affected family members showed decrements in how the brain
responds to negative consequences of behavior when compared with alcoholics lacking
such histories (Fein and Chang 2008).

Figure
3. Role of family history in cognition. Mean performances on the cognitive
neuropsychological test clusters for family history–positive (FH+)
and family history–negative (FH-) alcoholics and nonalcoholic
peer control subjects. Standard scores were based on the nonalcoholic FH-
group. The FH+ alcoholics differed significantly from the FH-
control subjects on all of the performance clusters. The FH- alcoholics
and FH+ control subjects did not differ significantly from each other
on any of the clusters.

SOURCE: Derived from Parsons 1989.

Psychiatric Comorbidity

Psychiatric Comorbidity As a Risk
Factor for Alcohol Dependence

People with AUDs frequently meet criteria
for other psychiatric disorders as well. For example, early data gathered through
the National Institute of Mental Health Epidemiological Catchment Area Project
revealed significant levels of comorbidity, with 3.8 percent of those with a lifetime
diagnosis of alcohol dependence also meeting criteria for a lifetime diagnosis
for a major psychotic disorder (Regier et al. 1990). More recent data from the
National Epidemiologic Survey on Alcohol and Related Conditions (NESARC) reveal
that among individuals with alcohol dependence, 15.15 percent and 17.75 percent
also met criteria for a depressive disorder or anxiety diagnosis, respectively
(Grant et al. 2004). Personality disorders also are common among alcoholics. For
example, alcoholics are reportedly 21 times more likely to have a diagnosis of
antisocial personality disorder (ASPD) than are nonalcoholics (Reiger et al. 1990).
Further, people with ASPD appear to be at greater risk for severe AUDs (i.e.,
more criteria for lifetime abuse and dependence met, greater frequency of heavy-drinking
days) compared with people with a conduct disorder diagnosis without ASPD or those
who met criteria for ASPD without conduct disorder prior to age 15 (Goldstein
et al. 2007). Interestingly, these authors conclude that the relationship between
ASPD and AUDs is similar for men and women.

Given these rates of comorbidity,
it is important to consider the extent to which AUDs may be causally related to
other diagnoses. For example, do people drink because they are depressed or are
they depressed because they drink? Similarly, do people with social anxiety and
alcohol problems reduce drinking when the anxiety is treated? Despite this entanglement
of AUDs with other psychiatric disorders, it is evident that the development of
alcohol dependence is not contingent on the presence of another psychiatric disorder.
That is, alcohol dependence may develop in individuals without other disorders,
and individuals with other diagnoses do not necessarily develop AUDs. However,
common genetic and environmental factors, as well as gene-by-environment interaction,
may place individuals with alcohol disorders at a higher risk for psychiatric
disorders compared with those without such comorbidities. Thomas and colleagues
(2008) recently addressed this complexity. They reported that among patients with
both social anxiety and alcohol use problems, pharmacological treatment of social
anxiety resulted in reduced anxiety symptoms but did not reduce drinking. However,
it did reduce the percentage of times that study participants reported drinking
to reduce anxiety. Thus, at least for this sample, there was a dissociation between
levels of social anxiety and alcohol consumption, even among those who believed
they used alcohol to “reduce social fears.”

In summary, there
is high comorbidity between AUDs and other psychiatric disorders. Determining
to what extent the onset of one precedes or follows another is complicated by
overlapping symptomatology, individual differences in symptom onset, and methods
of reporting. From a clinical perspective, it is clear that regardless of order
of onset, multiple disorders must be treated individually and cooperatively (McGovern
and McLellan 2008). Treating only one of the disorders is unlikely to produce
effective psychiatric recovery (Grant et al. 2004).

Psychiatric Comorbidity
As a Factor in Alcohol’s Effect on the Brain

There is a rich literature
considering the interaction of alcoholism and other major psychiatric disorders
on neuro­cognitive function (Glenn et al. 1993; Maurage et al. 2008; Thoma
et al. 2007; Uekermann et al. 2003). In an older, yet methodologically interesting,
study, Nixon and colleagues (1996) examined a limited number of cognitive processes
in dually diagnosed schizophrenic inpatients. In contrast to many cross-sectional
studies, they were able to recruit four study groups: three groups of inpatients
(schizophrenics, those with AUDs, and those with both a schizophrenia and an AUD
diagnosis) as well as a group of community control subjects. Consistent with the
heterogeneity in the field, control subjects were generally, although not always,
significantly superior to the other groups. Of more immediate interest was the
finding that dually diagnosed schizophrenics were not more impaired than schizophrenics
without an AUD. Although this result is somewhat counterintuitive, it is consistent
with other studies suggesting that schizophrenics who develop substance use disorders
(excluding nicotine) may have improved interpersonal skills relative to their
nonaddicted cohorts (Dixon et al. 1991).

Comorbid personality disorders
have been systematically examined less frequently, with the exception of ASPD
(Bauer and Hesselbrock 1999; Ceballos et al. 2003; Costa et al. 2000; Stevens
et al. 2001). Some researchers have argued that much of the presumed alcohol-related
cognitive compromise actually is attributable to underlying ASPD. This perspective
may be particularly relevant when dependent variables associated with behavioral
inhibition and impulse control are considered. However, these types of variables
are not the only ones impacted by ASPD status. Ceballos and colleagues (2003)
examined semantic processing ability in alcoholics and nonalcoholics with and
without ASPD. Regression analyses showed that being alcoholic and having ASPD
resulted in poorer semantic processing compared with control subjects. These results
suggest that although alcohol dependence and ASPD are frequently comorbid, neurocognitive
changes seen in recently sober alcoholics cannot be accounted for by ASPD status
alone.

Brain function is affected by both alcohol dependence and other
psychiatric disorders. However, when the influence of comorbid conditions is accounted
for, most studies reveal changes in brain structure and function associated with
alcohol dependence, separate from other disorders. As neuroimaging techniques
become increasingly sensitive, specific influences on particular brain systems,
especially within white matter connections, may be more evident.

Comorbid
Substance Use

Comorbid Substance Use As a Risk Factor in Alcohol Dependence

The co-occurrence of alcohol and other drug use disorders is well recognized.
Data analyzed from the National Comorbidity Survey revealed that 29.5 percent
of men and 34.7 percent of women who met criteria for alcohol dependence also
were drug dependent (Kessler et al. 1997). Importantly, AUDs were found to precede
drug problems in 25.6 percent of men and 20.0 percent of women (Kessler et al.
1997). NESARC data reveal a positive and significant relationship between current
alcohol use and specific drug disorders such as cocaine dependence (Stinson et
al. 2005), suggesting that alcohol use increased the risk for other drug use disorders.
Other studies that used NESARC data and controlled for sociodemographic characteristics
found that people with alcohol dependence were almost 19 times more likely than
people without alcohol dependence to meet criteria for drug dependence in the
last 12 months. When controlling for comorbid psychiatric disorders, people with
alcohol dependence were 7.5 times more likely than others to have a drug dependence
diagnosis (Hasin et al. 2007).

Nicotine use disorder, demonstrated primarily
through tobacco cigarette smoking, also commonly co-occurs with AUDs. People with
nicotine use disorder are two to three times more likely to be diagnosed with
AUDs, and a current diagnosis of either increases risk for being diagnosed with
the other in the future (Grucza and Bierut 2006; Sher et al. 1996). The rate of
tobacco use among treatment-seeking alcoholics and other substance abusers is
roughly three times that of the general population with rates ranging from 76
percent to more than 90 percent (Collins and Marks 1995; DiFranza and Guerrera
1990). Ceballos and colleagues (2006) reported similar prevalence data (see figure
4). Research with adolescents suggests that alcohol, drug (i.e., marijuana), and
smoking behaviors frequently develop around the same time (Faeh et al. 2006).

Data from the COGA project showed that variations in certain genetic factors
may contribute to risk for a particularly severe form of alcohol dependence and
comorbid drug dependence (Dick et al. 2007). People with alcohol dependence and
comorbid drug dependence displayed earlier age of onset of regular drinking, higher
rates of ASPD, conduct disorder, and novelty seeking. Thus, Dick and colleagues
(2007) suggested that the gene variant may be linked to behavioral disinhibition
traits rather than drug use, per se. This type of association is consistent with
findings reported by Faeh and colleagues (2006). Of particular clinical relevance
are recent findings revealing that people with comorbid alcohol and other drug
disorders are more likely to seek treatment than those with an alcohol disorder
alone (Stinson et al. 2005).

Figure
4. Prevalence of smoking in treatment-seeking substance-abusing subgroups.
Alc = alcohol use disorder; Alc/All = alcohol and any drug use disorder; Alc/Mar
= alcohol and marijuana use disorders; Alc/Stim = alcohol and stimulant use disorders;
Control = community comparison group with no psychiatric disorder and no substance
use disorder.

Comorbid Substance Use Disorders As a
Factor in Alcohol’s Effects on the Brain

The strong association
between alcohol and tobacco use may be mediated through several variables, including
the activation of underlying brain reward systems. In addition, the cognitive
enhancing effect of acute nicotine may contribute to the high levels of comorbidity.
Whereas alcohol dependence is associated with subtle, yet significant, cognitive
dysfunction, acute nicotine is known to enhance cognition, particularly processes
associated with vigilance and attentional aspects of working memory (Heishman
1998; Rodway et al. 2000). Given the opposing effects, it follows that acute nicotine
may serve to compensate for deficits associated with alcohol dependence. If so,
the strong association between the use of the two substances may not lie entirely
in the reward systems or shared genetic risks but also in their functional interaction.
Recent data revealing that alcoholics are differentially sensitive to acute nicotine
compared with community smoking control subjects are consistent with this conclusion
(Nixon et al. 2007). These interactions have significant implications for the
use of aggressive nicotine replacement therapy, particularly in the early stages
of recovery when cognitive processes may be most compromised.

The effects
of chronic smoking (chronic nicotine use) on brain structure and function also
have been studied in alcoholics. These findings suggest that chronic smoking alcoholics
show decrements in neurocognitive functioning and anatomical brain structure as
compared to nonsmoking alcoholics (Durazzo et al. 2007). Further, these differences
persist through recovery from alcoholism (Durazzo et al. 2006). Thus, although
acute nicotine administration improved neurocognitive function in alcoholics (e.g.,
Nixon et al. 2007), chronic cigarette smoking is associated with decrements in
brain structure and function. These data suggest that smoking effects may be quite
different than the effects of nicotine itself.

Age

Age As a Risk
Factor in Alcohol Dependence

Much of the literature concerning age as
a risk for alcoholism has focused on defining two subtypes of alcoholics, those
who were dependent on alcohol at an early age (before age 25) (Gilman et al. 2007;
Glenn and Nixon 1991, 1996; Roache et al. 2008) and those who develop alcoholism
later in life (Atkinson 2002; Atkinson et al. 2003). Early work suggested that
early-onset alcoholics were more likely than late-onset alcoholics to have job
problems, to be younger when they first drank alcohol, to have a higher rate of
maternal alcoholism, and to have childhood behavioral disorders and antisocial
behaviors (Glenn and Nixon 1991, 1996). Recent data further reinforce the importance
of early age of drinking by demonstrating that people who have their first drink
prior to age 15 are more likely than others to develop an AUD (Dawson et al. 2008).

In contrast, those who develop alcohol problems after age 60 are characterized
by having more biomedical versus psychosocial consequences, compared with early-onset
alcoholics, and are more likely to have alcohol– medication interactions
(Atkinson 2002). Further, later-onset alcoholics are likely to have a history
of heavy alcohol use and meet dependence criteria attributed to life stress or
psychiatric comorbidity (Atkinson 2002; Brennan et al. 1999; Schutte et al. 1998).
NESARC data reveal that the prevalence of alcoholism in older individuals may
be increasing, possibly following general population trends (Hasin et al. 2007;
National Projections Program, U.S. Bureau of the Census 2008.)

Age As
a Factor in Alcohol’s Effects on the Brain

The potential interaction
of chronic alcoholism and brain aging, also referred to as the premature aging
hypothesis, has been a long-standing research interest. One version of the hypothesis
suggested that chronic alcoholism prematurely aged the brains of young adults.
Findings from early studies suggesting that brain structure and function in young
alcoholics resembled older normal control subjects were consistent with this conclusion
(Blusewicz et al. 1977; Holden et al. 1988; Graff-Radford et al. 1982). Most of
these studies, however, used cross-sectional designs. Although some alcohol-related
brain changes were similar to those caused by aging, age-appropriate control subjects
were not always included, and thus it was difficult to conclude that the differences
were not the result of factors other than alcohol exposure. Continuing research
regarding this important question generally suggests that alcoholism, per se,
does not cause premature aging in younger drinkers (Oscar-Berman 2000; Oscar-Berman
and Marinkovi 2007).

This conclusion does not eliminate an interaction
of alcohol and aging. The alternative version of the premature aging hypothesis
suggests that older drinkers may be more sensitive to the neurotoxic effects of
alcohol than younger drinkers. This hypothesis has been supported by a number
of studies that accounted for quantity and frequency of use as well as drinking
occasions and number of acute withdrawals (Oscar-Berman and Marinkovi 2003). That
is, even when alcohol exposure, per se, can be statistically controlled for, older
alcoholics show greater effects. This susceptibility has been particularly evident
in structural brain-imaging studies (see the article by Rosenbloom and Pfefferbaum
in this issue, pp. 362–376) (e.g., Pfefferbaum et al. 1992, 1996, 1997)
and more specifically with analysis of white matter microstructural integrity
(Pfefferbaum et al. 2006). Some studies also have suggested gender-by-age interactions.
For example, men showed significant associations between age and decrements in
prefrontal and entire cortical gray matter, sulcal volume, and third ventricular
volume (Pfefferbaum et al. 1997, 2001), whereas the association between ventricular
expansion and advancing age were prominent in alcoholic women.

Overview
and Summary

?In summary, the risk for developing alcoholism and the resultant
negative consequences of alcohol dependence are influenced by a variety of factors
in addition to the quantity and frequency of alcohol consumed. Gender, family
history, comorbid psychiatric and substance use disorders, and age can impact
the development and outcome of alcoholism. This fact significantly complicates
the study of alcohol dependence. Ideally, we would construct a straight-forward
diagram depicting the interaction of these variables and describing categories
into which they might be placed, such as genetic factors, or family factors, or
environmental factors. The reality of the complexity of these interactions, however,
prohibits readable, meaningful illustration. For example, increasing age generally
is associated with decreased risk. However, cohort studies suggest that increasing
age might be less protective than it once was. Thus, the interaction of social–cultural
issues associated with our current response to healthy aging may reverse the previously
reported protective factors of aging. Furthermore, psychiatric comorbidity cannot
be comprehensively considered independent of family histories and gender. Although
the modulators discussed in this article do not form an “endless”
circle, they certainly form a complex system of interconnected factors that eludes
illustration.

Despite the difficulties associated with such a complex
system, it does identify multiple points of intervention, prevention, and treatment.
More specifically, the complexity suggests that there is no single point at which
such efforts might be effective. Rather, treatment (broadly defined) may occur
at various or multiple intersections and may include behavioral, sociocultural,
and pharmacologic interventions. However, to most effectively identify these intersections
and treatment modalities, programmatic hypothesis-driven research must be applied.

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